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Small and midsize businesses can use AI for practical, recurring work—not just large-scale enterprise projects. The strongest case is to start with one workflow, keep people responsible for consequential decisions, and measure whether the change improves time, quality, or capacity. AI can help, but reported benefits are not guaranteed business returns.
AI adoption is rising, but small businesses are not all in the same place
Across OECD member countries, the share of firms using AI grew from 5.6% in 2020 to 14% in 2024, according to the OECD’s 2025 discussion paper AI adoption by small and medium-sized enterprises. Adoption remains relatively low compared with other digital technologies and is less common among smaller firms than larger ones.
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A separate OECD report, Generative AI and the SME Workforce, draws on a representative survey of more than 5,000 SMEs in Austria, Canada, Germany, Ireland, Japan, Korea, and the United Kingdom. In that specific seven-country survey, conducted in late 2024, 31% of SMEs said they used generative AI for work. Reported use ranged from 24% in Japan to 39% in Germany. Those figures describe the surveyed countries and should not be treated as a universal estimate for every SMB.
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Firm size also matters: 23.6% of one-person businesses reported generative-AI use, compared with 45.8% of the largest SMEs in the survey. The OECD’s point that “Generative AI has democratised the use of AI” describes broader access to tools; it does not mean every business has equal skills, infrastructure, or results.
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What SMEs using generative AI say they gain
Among SMEs that reported using generative AI, the OECD survey found these self-reported effects:
| Reported effect | Share of AI-using SMEs |
|---|---|
| Improved employee performance | 65% |
| Ability to scale up | 35% |
| Improved ability to compete with larger companies | 29% |
| Increased revenue | 26% |
These are respondents’ reported experiences, not proof that generative AI caused the outcomes or that another business will see the same results. Microsoft Research’s July 2024 synthesis of workplace studies likewise says effects vary by role, function, organization, and how tools are adopted and used: The Effects of Generative AI on High-Skilled Work.
The OECD also estimates that AI could add 0.2–1.3 percentage points to annual labour-productivity growth across G7 economies over the next decade. That is an economy-level estimate, not a forecast of productivity or return on investment for an individual SMB.
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The OECD describes four SME profiles based on digital maturity, the complexity of AI use, and how widely it is used. They are ways to think about fit—not a mandatory ladder every company must climb.
| Profile | Typical pattern | What it means for a business |
|---|---|---|
| AI Novices | Use embedded tools for peripheral tasks | A contained, low-consequence task may be a sensible place to learn. |
| AI Explorers | Develop bespoke solutions | Custom work can address particular needs, but requires the capability to build and maintain it. |
| AI Optimisers | Integrate tools across functions | Broader use calls for more coordination, reliable processes, and staff skills. |
| AI Champions | Embed AI across operations and strategy | AI becomes part of how the business operates and makes plans. |
A smaller first step is not a failure to innovate. It can reveal whether a task is suitable, whether the output is dependable, and what support employees need before the business commits to wider integration.
How to identify and test a useful AI task
- Find a recurring bottleneck. Ask which regular task consumes staff time or depends on a skill the team lacks. Be specific about the work and its current process rather than starting with a tool search.
- Check the task’s fit and stakes. Consider how complex the work is and what happens if an answer is wrong. Keep human review in workflows where mistakes could materially affect customers, finances, or business decisions.
- Check data and operating requirements. Determine whether the necessary information is accessible, accurate, and appropriate to use; whether it is sensitive; and whether connectivity, computing resources, and implementation or ongoing costs are manageable.
- Prepare people and safeguards. Identify who will use and review the tool, what training they need, and how the business will check accuracy, harmful outputs, and relevant legal obligations. Buying access does not supply these capabilities automatically.
- Set a baseline, then run a bounded trial. Record how the task works now and choose an outcome to measure, such as completion time, quality, or capacity. Test the AI-assisted process on a limited scope and compare it with that baseline.
- Decide whether to expand. Use the results, staff feedback, costs, and any errors or risks to decide whether the task merits continued use, adjustment, or a broader rollout. The OECD evidence supports these readiness considerations, but does not prescribe a universal implementation sequence.
Build readiness before expanding use
The OECD identifies four enabling conditions for SME AI adoption: connectivity, AI-enabling inputs, skills, and finance. A business does not need to solve every constraint at once, but it should know which one could undermine a proposed use case.
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- Connectivity: Can the team reliably access the service where and when the work happens?
- AI-enabling inputs: Is the information the task depends on available and suitable for the intended use?
- Skills: Can employees use the tool, judge its output, and recognize when to escalate or verify it? Skills remain important even when AI is available.
- Finance: Can the business cover implementation and ongoing costs without assuming that reported gains will materialize?
Training is a practical part of readiness, not an optional add-on. The OECD identifies skills shortages as a barrier and reports that SMEs welcome training support. A useful training plan should match the work people will actually do, including how to review outputs and handle mistakes.
Manage errors, harmful outputs, and legal uncertainty
The OECD identifies accuracy, harmful content, and legal uncertainty among challenges SMEs report when adopting AI. These are operational concerns: an output that sounds confident may still be wrong or unsuitable, and the business remains responsible for deciding what enters its workflow.
- Require a knowledgeable person to check outputs when errors matter.
- Set boundaries for what information employees may enter into AI services, especially where data is sensitive.
- Test outputs against the task’s requirements before using them with customers or in consequential decisions.
- Make clear who reviews the work and what to do when an answer is questionable.
- Assess applicable legal obligations for the specific use and location rather than assuming a tool’s availability settles the question.
These safeguards cannot make every use risk-free; they make responsibility and review part of the workflow instead of treating generated output as automatically reliable.
AI gains do not automatically mean fewer staff
In the OECD’s seven-country survey, 83% of SMEs using generative AI said it had no effect on their overall staff needs; 9% reported a decrease and 6% an increase. Respondents were not asked how large those changes were, so the figures cannot be used to calculate a net employment effect.
The survey also indicates that skill needs can rise as firms use generative AI. For an SMB, a reasonable goal may be to improve performance, expand capacity, or free employees for other work—not to assume that productivity claims translate directly into headcount cuts.
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